Linear regression is the most common form of regression analysis. If the assumptions of normality, homogeneity and independence of random errors are satisfied, the least square method gives exact results. In practice, assumptions are often difficult to reach or even elusive, which requires looking for an alternative. Resampling methods, based on the law of large numbers, are offered as a possible potential solution. In this paper, the bootstrap method is studied, which is based on the idea that a statistical sample already contains all available information about a distribution function F, so by resampling, we get a sample that belongs to that same distribution. Two algorithms are particularly discussed – the data resampling method and the errors resampling method. In the first chapter, the basic concepts of probability theory and the bootstrap method are presented. In the second chapter, a simple linear regression model is defined and the idea of the bootstrap method for the distribution of estimators is exposed. The third chapter is an extension of the second chapter to a multidimensional case. Also, the concept of prediction and the role of the bootstrap method in this respect are presented. In the fourth chapter, the practical results obtained by conducting various simulations in the R programming language are exposed. The code used to perform the simulations is presented at the end of the paper, in the fifth chapter.